Items with Index Term: multilayer

Aghili, S. A.; Khanzadi, M.; Haji Mohammad Rezaei, A. and Rahbar, M. (2026) Data-driven approach to fault detection for hospital HVAC system. Smart and Sustainable Built Environment, 15(2), pp. 765-788. ISSN 2046-6099

Ahiaga-Dagbui, D and Smith, S D (2012) Neural networks for modelling the final target cost of water projects. In: Smith, S D (ed.) Proceedings of 28th Annual ARCOM Conference, 3-5 September 2012, Edinburgh, UK.

Al-Bazi, A and Dawood, N (2018) Simulation-based optimisation using simulated annealing for crew allocation in the precast industry. Architectural Engineering and Design Management, 14(1-2), pp. 109-126. ISSN 1745-2007

Andalib, M (2026) Predictive modelling for contract selection in construction through data mining and machine learning: Insights from agency theory. International Journal of Construction Education and Research, 22(1), pp. 138-163. ISSN 1557-8771

Bayram, S and Al-Jibouri, S (2016) Efficacy of estimation methods in forecasting building projects' costs. Journal of Construction Engineering and Management, 142(11): 05016012, ISSN 0733-9364

Capace, Brunella (2019) NDT application in transport asset management: Qa/qc performance specifications in pavement construction and maintenance. PhD thesis, Università degli Studi di Catania, Italy.

Castelblanco, G; Fenoaltea, E M; De Marco, A; Demagistris, P; Petruzzi, S and Zeppegno, D (2024) Combining stakeholder and risk management: Multilayer network analysis for complex megaprojects. Journal of Construction Engineering and Management, 150(2): 04023161, ISSN 0733-9364

Cotella, V A; Neagu, C D; Bavani, S A; Trichard, M; Horcholle, F; Sangoï, R; Lacalle, C; Jeanvoine, A; Sparrow, T and Wilson, A S (2025) Optimising 3D point cloud semantic segmentation: ML and manual refinement in the unesco saltaire village. Building Research & Information, 53(7), pp. 846-870. ISSN 0961-3218

Edwards, D J; Yang, J; Wright, B C and Love, P E D (2007) Establishing the link between plant operator performance and personal motivation. Journal of Engineering, Design and Technology, 5(2), pp. 173-187. ISSN 1726-0531

Egwim, C N; Alaka, H; Pan, Y; Balogun, H; Ajayi, S; Hye, A and Egunjobi, O O (2025) Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors. Journal of Engineering, Design and Technology, 23(2), pp. 640-665. ISSN 1726-0531

Elshaboury, N; Mohammed Abdelkader, E and Al-Sakkaf, A (2025) Convolutional neural network-based deep learning model for air quality prediction in october city of Egypt. Construction Innovation, 25(2), pp. 620-640. ISSN 1471-4175

Elshaboury, N; Mohammed Abdelkader, E; Al-Sakkaf, A and Bagchi, A (2025) A deep convolutional neural network for predicting electricity consumption at grey nuns building in Canada. Construction Innovation, 25(2), pp. 270-289. ISSN 1471-4175

Heravi, G and Eslamdoost, E (2015) Applying artificial neural networks for measuring and predicting construction-labor productivity. Journal of Construction Engineering and Management, 141(10): 04015032, ISSN 0733-9364

Horne, R; Maller, C and Dalton, T (2014) Low carbon, water-efficient house retrofits: An emergent niche? Building Research & Information, 42(4), pp. 539-548. ISSN 0961-3218

Hosseinian, S M and Jaberi, A (2024) Optimal sharing of construction project outcomes with downstream contracting parties: Principal-agent analysis. Journal of Construction Engineering and Management, 150(2): 04023156, ISSN 0733-9364

Hou, X; Zeng, Y and Xue, J (2020) Detecting structural components of building engineering based on deep-learning method. Journal of Construction Engineering and Management, 146(2): 04019097, ISSN 0733-9364

Hruska, R C (2023) A functional all-hazard approach to critical infrastructure dependency analysis. PhD thesis, University of Idaho, USA.

Kale, S and Karaman, E A (2011) Evaluating the knowledge management practices of construction firms by using importance-comparative performance analysis maps. Journal of Construction Engineering and Management, 137(12), pp. 1142-1152. ISSN 0733-9364

Kantianis, D D (2022) Design morphology complexity and conceptual building project cost forecasting. Journal of Financial Management of Property and Construction, 27(3), pp. 387-414. ISSN 1366-4387

Karaiskos, P; Munian, Y; Martinez-Molina, A and Alamaniotis, M (2026) Indoor air quality prediction modeling for a naturally ventilated fitness building using rnn-LSTM artificial neural networks. Smart and Sustainable Built Environment, 15(1), pp. 384-406. ISSN 2046-6099

Karki, S and Hadikusumo, B (2023) Machine learning for the identification of competent project managers for construction projects in Nepal. Construction Innovation, 23(1), pp. 1-18. ISSN 1471-4175

Laura-Portugal, C. and Hammad, A. (2026) Deep learning-based forecasting for construction project duration at completion. International Journal of Construction Management, 26(8), pp. 1543-1561. ISSN 1562-3599

Lawal, H S; Ahmadu, H A; Abdullahi, M; Yamusa, M A and Abdulrazaq, M (2023) Modeling duration of building renovation projects. Journal of Financial Management of Property and Construction, 28(3), pp. 423-438. ISSN 1366-4387

Luo, X; Li, X; Song, X and Liu, Q (2023) Convolutional neural network algorithm-based novel automatic text classification framework for construction accident reports. Journal of Construction Engineering and Management, 149(12): 04023128, ISSN 0733-9364

Meng, Q and Zhu, S (2022) Construction activity classification based on vibration monitoring data: A supervised deep-learning approach with time series randaugment. Journal of Construction Engineering and Management, 148(9): 04022090, ISSN 0733-9364

Naoui, M A; Lejdel, B; Ayad, M; Amamra, A and kazar, O (2021) Using a distributed deep learning algorithm for analyzing big data in smart cities. Smart and Sustainable Built Environment, 10(1), pp. 90-105. ISSN 2046-6099

Obasi, S N N; Pemberton, J and Awe, O O (2025) A comparative study of soil classification machine learning models for construction management. International Journal of Construction Management, 25(5), pp. 584-593. ISSN 1562-3599

Oguz Erkal, E D; Hallowell, M R; Ghriss, A and Bhandari, S (2024) Predicting serious injury and fatality exposure using machine learning in construction projects. Journal of Construction Engineering and Management, 150(3): 04023169, ISSN 0733-9364

Ottaviani, F M; De Marco, A; Narbaev, T and Ballesteros-Pérez, P (2026) Work rate-based indicators for improving project performance regression models. International Journal of Construction Management, 26(1), pp. 99-112. ISSN 1562-3599

Peltola, T and Mansikkamäki, P (2007) Formable multilayer PCB structure: Design and technology demonstrator. Journal of Engineering, Design and Technology, 5(2), pp. 148-158. ISSN 1726-0531

Pemsel, S (2012) Knowledge processes and capabilities in project-based organizations. PhD thesis, Lund University, Sweden.

Petroutsatou, K; Georgopoulos, E; Lambropoulos, S and Pantouvakis, J P (2012) Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), pp. 679-687. ISSN 0733-9364

Ryu, J; Seo, J; Jebelli, H and Lee, S (2019) Automated action recognition using an accelerometer-embedded wristband-type activity tracker. Journal of Construction Engineering and Management, 145(1): 04018114, ISSN 0733-9364

Shabani Ardakani, S and Nik-Bakht, M (2021) Functional evaluation of change order and invoice management processes under different procurement strategies: Social network analysis approach. Journal of Construction Engineering and Management, 147(1): 1974, ISSN 0733-9364

Shirazi, D H and Toosi, H (2023) Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(4): 04023011, ISSN 0733-9364

Tam, C M; Tong, T K L and Tse, S L (2002) Artificial neural networks model for predicting excavator productivity. Engineering, Construction and Architectural Management, 9(5-6), pp. 446-452. ISSN 0969-9988

Vojinovic, Z and Kecman, V (2001) Modelling empirical data to support project cost estimating: Neural networks versus traditional methods. Construction Innovation, 1(4), pp. 227-243. ISSN 1471-4175

Wang, P; Wang, K; Huang, Y and Fenn, P (2024) A contingency approach for time-cost trade-off in construction projects based on machine learning techniques. Engineering, Construction and Architectural Management, 31(11), pp. 4677-4695. ISSN 0969-9988

Wang, S; Hasan, M and Lu, M (2024) Global sensitivity analysis methodology for construction simulation models: Multiple linear regressions versus multilayer perceptions. Journal of Construction Engineering and Management, 150(5): 04024035, ISSN 0733-9364

Xiahou, X; Li, Z; Xia, J; Zhou, Z and Li, Q (2023) A feature-level fusion-based multimodal analysis of recognition and classification of awkward working postures in construction. Journal of Construction Engineering and Management, 149(12): 04023138, ISSN 0733-9364

Zhang, P; Sing, M C P; Guo, S; Chan, I Y S and Fung, I W H (2025) Causal factors of near misses and accidents in urban railway construction: A complex network approach. Journal of Construction Engineering and Management, 151(7): 04025081, ISSN 0733-9364

Zhou, J; Zheng, X; Wang, F and Tian, D (2025) Intelligent identification approach for accident causation in hydraulic and hydropower engineering construction. Journal of Construction Engineering and Management, 151(12): 04025195, ISSN 0733-9364